Since 2016, nearly all text simplification systems have been sequence-to-sequence (seq2seq) models
trained end-to-end, which have greatly increased the fluency of the outputs [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. However, these systems mostly rely on deletion and tend to generate very short outputs at the cost of meaning preservation [7]}.
Table REF  shows that they neither split sentences nor paraphrase well as reflected by the low percentage of splits (\(<\)  1%) and new words introduced (\(<\)  11.2%).
While deleting words is a viable (and the simplest) way to reduce the complexity of sentences, it is suboptimal and unsatisfying. Professional editors are known to use a sophisticated combination of deletion, paraphrasing, and sentence splitting to simplify texts [8]}.
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